S.SHUBHAM SINGHDATA ENGINEER / MUMBAILet’s talk ↗

DATA ENGINEERING & APPLIED AI

Hi, I’m Shubham.
I make data
make sense.

I build the pipelines, models and AI-assisted tools that help people find answers in their data—and understand where those answers came from.

MUMBAI, INDIAOPEN TO UAE OPPORTUNITIES
SELECTED WORK & EXPERIENCESCROLL TO EXPLORE ↓

THE THROUGHLINE / AN INTERACTIVE VIEW

From raw data
to real context.

Every useful answer has a journey behind it. Select a stage to explore how I think about that journey.

Explore the work behind it ↘
DATA JOURNEY / CONCEPTUAL VIEW01 — 04

01 / SOURCE

Start with the origin.

Understand where the data comes from, what it represents, and what downstream teams need from it.

A CONCEPTUAL VIEW OF MY APPROACH · NO LIVE DATA
01 / INTRODUCTIONTHE PERSON BEHIND THE WORK

I like the questions
that start with why.

The most interesting part of data engineering is often the part that happens before anyone writes a query.

When an analyst asks why a number looks wrong, the answer isn’t always in the final table. It can be in the source, a transformation, a business rule, or a missing piece of context.

That’s the kind of problem I work on. At General Mills, I’ve moved from supporting enterprise data pipelines to building data models and leading an applied AI initiative around metadata. I care about systems people can investigate, understand and use.

My background is in data engineering, but I’m drawn to the space where reliable data, human questions and useful AI meet.

See my journey ↘
02 / SELECTED WORKTHE PROBLEM · MY CONTRIBUTION · THE THINKING

What I’ve worked on.

Seven areas of work, from enterprise data foundations to applied AI. Choose one to see the story behind it.

PROFESSIONAL EXPERIENCE
GENERAL MILLS / MUMBAI
01 / APPLIED AI & GOVERNANCEPRODUCTION

Giving data the context it deserves.

A useful description starts with more than a column name.

THE CHALLENGE

When a data question arrives at the final table, tracing it back to the source and understanding the transformations takes time. Analysts and AI tools need context that a field name alone cannot provide.

MY CONTRIBUTION

I originated and led an applied AI initiative that uses data context to help generate table and column descriptions, with human review and feedback. It evolved from early prototypes into a production solution used across teams.

THE ENGINEERING THINKING

Connect lineage with meaningful metadata, evaluate draft descriptions, and keep people responsible for approval before publication.

PYTHONMETADATAAPPLIED AIHUMAN REVIEW

Descriptions are intentionally high-level and omit internal data and unapproved metrics.

03 / CAPABILITIESWHAT I REACH FOR

A toolkit for
the whole journey.

From data arriving at the platform to the models and tools people use to answer questions.

01 / BUILD

Data engineering

Designing and operating pipelines that move and transform data for analytics.

Python · SQL · Airflow · Cloud Composer · ETL / ELT
02 / MODEL

Analytics foundations

Building documented models and quality checks that make data easier to use.

BigQuery · dbt · Data modeling · Testing · SCD
03 / CONNECT

Applied AI & context

Exploring how AI can help people discover and interpret enterprise data.

Gemini · Vertex AI · Glean · Document AI · LLM integration
04 / UNDERSTAND

Governance & reliability

Following the data, identifying issues and giving people context they can review.

Lineage · Metadata · Data quality · Monitoring · Reconciliation

Also familiar with PySpark, AWS and Databricks at a basic level.

04 / EXPERIENCEHOW I GOT HERE

Learning by
building.

My path at General Mills has grown from supporting a data platform to designing models and leading applied AI work.

GM

General Mills

MUMBAI, INDIA · ENTERPRISE DATA

2023 — PRESENT

Data Engineer I

Continuing to build data foundations and applied AI solutions for enterprise users.

Associate Data Engineer

Worked across metadata, AI-assisted analytics, pipeline modernization, data modeling and quality.

Data Engineer Trainee

Supported enterprise data pipelines, production troubleshooting and source-data migration.

Data Engineer Intern

Contributed to an enterprise data platform covering ingestion, transformation and analytics.

05 / BACKGROUNDEDUCATION & MILESTONES

The foundations
behind the work.

Learning has always been part of the job—whether in a classroom, a hackathon or a production issue.

EDUCATION

Master of Computer Applications

Vivekanand Education Society’s Institute of Technology · University of Mumbai · 2023

B.Sc. Computer Science

Thakur Ramnarayan College of Arts & Commerce · University of Mumbai · 2021

SELECTED RECOGNITION

Winner · Codeicon 4.0 Hackathon
With Team Data Miners, explored sentiment analysis and built a Streamlit demonstration to share insights and predicted product ratings.

Top 100 globally · Meta Hackathon
Finalist · Nomura KakushIn Hackathon

06 / LET’S CONNECT

Have a problem
worth exploring?

I’m interested in data engineering, analytics engineering and applied AI opportunities, including in the UAE.